{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "8d1e0f7a",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-05-24T01:18:09.128879Z",
     "iopub.status.busy": "2023-05-24T01:18:09.128366Z",
     "iopub.status.idle": "2023-05-24T01:18:37.274936Z",
     "shell.execute_reply": "2023-05-24T01:18:37.273946Z"
    },
    "executionInfo": {
     "elapsed": 2844,
     "status": "ok",
     "timestamp": 1684409199330,
     "user": {
      "displayName": "RUIYING CHEN",
      "userId": "09384040230747805046"
     },
     "user_tz": -480
    },
    "id": "wHsFZo6akD8M",
    "outputId": "d017ea14-1ff6-429f-d7da-2f6ad9e82ec1",
    "papermill": {
     "duration": 28.158638,
     "end_time": "2023-05-24T01:18:37.277570",
     "exception": false,
     "start_time": "2023-05-24T01:18:09.118932",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "import torch\n",
    "import torchvision\n",
    "import torchvision.transforms as transforms\n",
    "\n",
    "transform = transforms.Compose([transforms.ToTensor(), transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))])\n",
    "batch_size = 8\n",
    "train_dataset = torchvision.datasets.ImageFolder(\"/kaggle/input/d/aymenboulila2/cifar100/CIFAR100/TRAIN\", transform=transform)\n",
    "\n",
    "test_dataset = torchvision.datasets.ImageFolder(\"/kaggle/input/d/aymenboulila2/cifar100/CIFAR100/TEST\", transform=transform)\n",
    "\n",
    "train_loader = torch.utils.data.DataLoader(train_dataset, batch_size=batch_size, shuffle=True, num_workers=2)\n",
    "\n",
    "test_loader = torch.utils.data.DataLoader(test_dataset, batch_size=batch_size, shuffle=False, num_workers=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "f50d62c7",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-05-24T01:18:37.290550Z",
     "iopub.status.busy": "2023-05-24T01:18:37.289388Z",
     "iopub.status.idle": "2023-05-24T01:18:37.726244Z",
     "shell.execute_reply": "2023-05-24T01:18:37.725118Z"
    },
    "executionInfo": {
     "elapsed": 8,
     "status": "ok",
     "timestamp": 1684409199330,
     "user": {
      "displayName": "RUIYING CHEN",
      "userId": "09384040230747805046"
     },
     "user_tz": -480
    },
    "id": "VjkCK8NykPMb",
    "outputId": "d8b6db1d-275c-4979-f5c6-411c763dea3f",
    "papermill": {
     "duration": 0.447808,
     "end_time": "2023-05-24T01:18:37.730804",
     "exception": false,
     "start_time": "2023-05-24T01:18:37.282996",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([8])\n"
     ]
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "# functions to show an image\n",
    "\n",
    "\n",
    "def imshow(img):\n",
    "    img = img / 2 + 0.5     # unnormalize\n",
    "    npimg = img.numpy()\n",
    "    plt.imshow(np.transpose(npimg, (1, 2, 0)))\n",
    "    plt.show()\n",
    "\n",
    "\n",
    "# get some random training images\n",
    "dataiter = iter(train_loader)\n",
    "# images, labels = dataiter.next()\n",
    "images, labels = next(dataiter)\n",
    "\n",
    "# show images\n",
    "imshow(torchvision.utils.make_grid(images))\n",
    "# print labels\n",
    "print(labels.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "a1eaf8d8",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-05-24T01:18:37.747128Z",
     "iopub.status.busy": "2023-05-24T01:18:37.746767Z",
     "iopub.status.idle": "2023-05-24T01:18:37.782574Z",
     "shell.execute_reply": "2023-05-24T01:18:37.781568Z"
    },
    "executionInfo": {
     "elapsed": 7,
     "status": "ok",
     "timestamp": 1684409199331,
     "user": {
      "displayName": "RUIYING CHEN",
      "userId": "09384040230747805046"
     },
     "user_tz": -480
    },
    "id": "1mY2iwmCkcpA",
    "papermill": {
     "duration": 0.046265,
     "end_time": "2023-05-24T01:18:37.784921",
     "exception": false,
     "start_time": "2023-05-24T01:18:37.738656",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "import math\n",
    "\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.nn.functional as F\n",
    "from torch.nn import init\n",
    "\n",
    "def conv3x3(in_planes, out_planes, stride=1):\n",
    "    \"3x3 convolution with padding\"\n",
    "    return nn.Conv2d(\n",
    "        in_planes, out_planes, kernel_size=3, stride=stride, padding=1, bias=False\n",
    "    )\n",
    "\n",
    "\n",
    "class BasicBlock(nn.Module):\n",
    "    expansion = 1\n",
    "\n",
    "    def __init__(\n",
    "        self, inplanes, planes, stride=1, downsample=None, use_triplet_attention=False\n",
    "    ):\n",
    "        super(BasicBlock, self).__init__()\n",
    "        self.conv1 = conv3x3(inplanes, planes, stride)\n",
    "        self.bn1 = nn.BatchNorm2d(planes)\n",
    "        self.relu = nn.ReLU(inplace=True)\n",
    "        self.conv2 = conv3x3(planes, planes)\n",
    "        self.bn2 = nn.BatchNorm2d(planes)\n",
    "        self.downsample = downsample\n",
    "        self.stride = stride\n",
    "\n",
    "        if use_triplet_attention:\n",
    "            self.triplet_attention = TripletAttention(planes, 16)\n",
    "        else:\n",
    "            self.triplet_attention = None\n",
    "\n",
    "    def forward(self, x):\n",
    "        residual = x\n",
    "\n",
    "        out = self.conv1(x)\n",
    "        out = self.bn1(out)\n",
    "        out = self.relu(out)\n",
    "\n",
    "        out = self.conv2(out)\n",
    "        out = self.bn2(out)\n",
    "\n",
    "        if self.downsample is not None:\n",
    "            residual = self.downsample(x)\n",
    "\n",
    "        if not self.triplet_attention is None:\n",
    "            out = self.triplet_attention(out)\n",
    "\n",
    "        out += residual\n",
    "        out = self.relu(out)\n",
    "\n",
    "        return out\n",
    "\n",
    "\n",
    "class Bottleneck(nn.Module):\n",
    "    expansion = 4\n",
    "\n",
    "    def __init__(\n",
    "        self, inplanes, planes, stride=1, downsample=None, use_triplet_attention=False\n",
    "    ):\n",
    "        super(Bottleneck, self).__init__()\n",
    "        self.conv1 = nn.Conv2d(inplanes, planes, kernel_size=1, bias=False)\n",
    "        self.bn1 = nn.BatchNorm2d(planes)\n",
    "        self.conv2 = nn.Conv2d(\n",
    "            planes, planes, kernel_size=3, stride=stride, padding=1, bias=False\n",
    "        )\n",
    "        self.bn2 = nn.BatchNorm2d(planes)\n",
    "        self.conv3 = nn.Conv2d(planes, planes * 4, kernel_size=1, bias=False)\n",
    "        self.bn3 = nn.BatchNorm2d(planes * 4)\n",
    "        self.relu = nn.ReLU(inplace=True)\n",
    "        self.downsample = downsample\n",
    "        self.stride = stride\n",
    "\n",
    "        if use_triplet_attention:\n",
    "            self.triplet_attention = TripletAttention(planes * 4, 16)\n",
    "        else:\n",
    "            self.triplet_attention = None\n",
    "\n",
    "    def forward(self, x):\n",
    "        residual = x\n",
    "\n",
    "        out = self.conv1(x)\n",
    "        out = self.bn1(out)\n",
    "        out = self.relu(out)\n",
    "\n",
    "        out = self.conv2(out)\n",
    "        out = self.bn2(out)\n",
    "        out = self.relu(out)\n",
    "\n",
    "        out = self.conv3(out)\n",
    "        out = self.bn3(out)\n",
    "\n",
    "        if self.downsample is not None:\n",
    "            residual = self.downsample(x)\n",
    "\n",
    "        if not self.triplet_attention is None:\n",
    "            out = self.triplet_attention(out)\n",
    "\n",
    "        out += residual\n",
    "        out = self.relu(out)\n",
    "\n",
    "        return out\n",
    "\n",
    "\n",
    "class ResNet(nn.Module):\n",
    "    def __init__(self, block, layers, network_type, num_classes, att_type=None):\n",
    "        self.inplanes = 64\n",
    "        super(ResNet, self).__init__()\n",
    "        self.network_type = network_type\n",
    "        # different model config between ImageNet and CIFAR\n",
    "        if network_type == \"ImageNet\":\n",
    "            self.conv1 = nn.Conv2d(\n",
    "                3, 64, kernel_size=7, stride=2, padding=3, bias=False\n",
    "            )\n",
    "            self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)\n",
    "            self.avgpool = nn.AvgPool2d(7)\n",
    "        else:\n",
    "            self.conv1 = nn.Conv2d(\n",
    "                3, 64, kernel_size=3, stride=1, padding=1, bias=False\n",
    "            )\n",
    "\n",
    "        self.bn1 = nn.BatchNorm2d(64)\n",
    "        self.relu = nn.ReLU(inplace=True)\n",
    "\n",
    "        self.layer1 = self._make_layer(block, 64, layers[0], att_type=att_type)\n",
    "        self.layer2 = self._make_layer(\n",
    "            block, 128, layers[1], stride=2, att_type=att_type\n",
    "        )\n",
    "        self.layer3 = self._make_layer(\n",
    "            block, 256, layers[2], stride=2, att_type=att_type\n",
    "        )\n",
    "        self.layer4 = self._make_layer(\n",
    "            block, 512, layers[3], stride=2, att_type=att_type\n",
    "        )\n",
    "\n",
    "        self.fc = nn.Linear(512 * block.expansion, num_classes)\n",
    "\n",
    "        init.kaiming_normal_(self.fc.weight)\n",
    "        for key in self.state_dict():\n",
    "            if key.split(\".\")[-1] == \"weight\":\n",
    "                if \"conv\" in key:\n",
    "                    init.kaiming_normal_(self.state_dict()[key], mode=\"fan_out\")\n",
    "                if \"bn\" in key:\n",
    "                    if \"SpatialGate\" in key:\n",
    "                        self.state_dict()[key][...] = 0\n",
    "                    else:\n",
    "                        self.state_dict()[key][...] = 1\n",
    "            elif key.split(\".\")[-1] == \"bias\":\n",
    "                self.state_dict()[key][...] = 0\n",
    "\n",
    "    def _make_layer(self, block, planes, blocks, stride=1, att_type=None):\n",
    "        downsample = None\n",
    "        if stride != 1 or self.inplanes != planes * block.expansion:\n",
    "            downsample = nn.Sequential(\n",
    "                nn.Conv2d(\n",
    "                    self.inplanes,\n",
    "                    planes * block.expansion,\n",
    "                    kernel_size=1,\n",
    "                    stride=stride,\n",
    "                    bias=False,\n",
    "                ),\n",
    "                nn.BatchNorm2d(planes * block.expansion),\n",
    "            )\n",
    "\n",
    "        layers = []\n",
    "        layers.append(\n",
    "            block(\n",
    "                self.inplanes,\n",
    "                planes,\n",
    "                stride,\n",
    "                downsample,\n",
    "                use_triplet_attention=att_type == \"TripletAttention\",\n",
    "            )\n",
    "        )\n",
    "        self.inplanes = planes * block.expansion\n",
    "        for i in range(1, blocks):\n",
    "            layers.append(\n",
    "                block(\n",
    "                    self.inplanes,\n",
    "                    planes,\n",
    "                    use_triplet_attention=att_type == \"TripletAttention\",\n",
    "                )\n",
    "            )\n",
    "\n",
    "        return nn.Sequential(*layers)\n",
    "\n",
    "    def forward(self, x):\n",
    "        x = self.conv1(x)\n",
    "        x = self.bn1(x)\n",
    "        x = self.relu(x)\n",
    "        if self.network_type == \"ImageNet\":\n",
    "            x = self.maxpool(x)\n",
    "\n",
    "        x = self.layer1(x)\n",
    "        x = self.layer2(x)\n",
    "        x = self.layer3(x)\n",
    "        x = self.layer4(x)\n",
    "\n",
    "        if self.network_type == \"ImageNet\":\n",
    "            x = self.avgpool(x)\n",
    "        else:\n",
    "            x = F.avg_pool2d(x, 4)\n",
    "        x = x.view(x.size(0), -1)\n",
    "        x = self.fc(x)\n",
    "        return x\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "27e73ae3",
   "metadata": {
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     "start_time": "2023-05-24T01:18:37.790978",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## 定义SKNet"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "926b43bf",
   "metadata": {
    "execution": {
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   "outputs": [],
   "source": [
    "\n",
    "#sknet\n",
    "class SKConv(nn.Module):\n",
    "    def __init__(self, features, M=2, G=32, r=16, stride=1 ,L=32):\n",
    "        \"\"\" Constructor\n",
    "        Args:\n",
    "            features: input channel dimensionality.\n",
    "            M: the number of branchs.\n",
    "            G: num of convolution groups.\n",
    "            r: the ratio for compute d, the length of z.\n",
    "            stride: stride, default 1.\n",
    "            L: the minimum dim of the vector z in paper, default 32.\n",
    "        \"\"\"\n",
    "        super(SKConv, self).__init__()\n",
    "        d = max(int(features/r), L)\n",
    "        self.M = M\n",
    "        self.features = features\n",
    "        self.convs = nn.ModuleList([])\n",
    "        for i in range(M):\n",
    "            self.convs.append(nn.Sequential(\n",
    "                nn.Conv2d(features, features, kernel_size=3, stride=stride, padding=1+i, dilation=1+i, groups=G, bias=False),\n",
    "                nn.BatchNorm2d(features),\n",
    "                nn.ReLU(inplace=True)\n",
    "            ))\n",
    "        self.gap = nn.AdaptiveAvgPool2d((1,1))\n",
    "        self.fc = nn.Sequential(nn.Conv2d(features, d, kernel_size=1, stride=1, bias=False),\n",
    "                                nn.BatchNorm2d(d),\n",
    "                                nn.ReLU(inplace=True))\n",
    "        self.fcs = nn.ModuleList([])\n",
    "        for i in range(M):\n",
    "            self.fcs.append(\n",
    "                 nn.Conv2d(d, features, kernel_size=1, stride=1)\n",
    "            )\n",
    "        self.softmax = nn.Softmax(dim=1)\n",
    "        \n",
    "    def forward(self, x):\n",
    "        \n",
    "        batch_size = x.shape[0]\n",
    "        \n",
    "        feats = [conv(x) for conv in self.convs]      \n",
    "        feats = torch.cat(feats, dim=1)\n",
    "        feats = feats.view(batch_size, self.M, self.features, feats.shape[2], feats.shape[3])\n",
    "        \n",
    "        feats_U = torch.sum(feats, dim=1)\n",
    "        feats_S = self.gap(feats_U)\n",
    "        feats_Z = self.fc(feats_S)\n",
    "\n",
    "        attention_vectors = [fc(feats_Z) for fc in self.fcs]\n",
    "        attention_vectors = torch.cat(attention_vectors, dim=1)\n",
    "        attention_vectors = attention_vectors.view(batch_size, self.M, self.features, 1, 1)\n",
    "        attention_vectors = self.softmax(attention_vectors)\n",
    "        \n",
    "        feats_V = torch.sum(feats*attention_vectors, dim=1)\n",
    "        \n",
    "        return feats_V\n",
    "\n",
    "\n",
    "class SKUnit(nn.Module):\n",
    "    def __init__(self, in_features, mid_features, out_features, M=2, G=32, r=16, stride=1, L=32):\n",
    "        \"\"\" Constructor\n",
    "        Args:\n",
    "            in_features: input channel dimensionality.\n",
    "            out_features: output channel dimensionality.\n",
    "            M: the number of branchs.\n",
    "            G: num of convolution groups.\n",
    "            r: the ratio for compute d, the length of z.\n",
    "            mid_features: the channle dim of the middle conv with stride not 1, default out_features/2.\n",
    "            stride: stride.\n",
    "            L: the minimum dim of the vector z in paper.\n",
    "        \"\"\"\n",
    "        super(SKUnit, self).__init__()\n",
    "        \n",
    "        self.conv1 = nn.Sequential(\n",
    "            nn.Conv2d(in_features, mid_features, 1, stride=1, bias=False),\n",
    "            nn.BatchNorm2d(mid_features),\n",
    "            nn.ReLU(inplace=True)\n",
    "            )\n",
    "        \n",
    "        self.conv2_sk = SKConv(mid_features, M=M, G=G, r=r, stride=stride, L=L)\n",
    "        \n",
    "        self.conv3 = nn.Sequential(\n",
    "            nn.Conv2d(mid_features, out_features, 1, stride=1, bias=False),\n",
    "            nn.BatchNorm2d(out_features)\n",
    "            )\n",
    "        \n",
    "\n",
    "        if in_features == out_features: # when dim not change, input_features could be added diectly to out\n",
    "            self.shortcut = nn.Sequential()\n",
    "        else: # when dim not change, input_features should also change dim to be added to out\n",
    "            self.shortcut = nn.Sequential(\n",
    "                nn.Conv2d(in_features, out_features, 1, stride=stride, bias=False),\n",
    "                nn.BatchNorm2d(out_features)\n",
    "            )\n",
    "        \n",
    "        self.relu = nn.ReLU(inplace=True)\n",
    "    \n",
    "    def forward(self, x):\n",
    "        residual = x\n",
    "        \n",
    "        out = self.conv1(x)\n",
    "        out = self.conv2_sk(out)\n",
    "        out = self.conv3(out)\n",
    "        \n",
    "        return self.relu(out + self.shortcut(residual))\n",
    "\n",
    "class SKNet(nn.Module):\n",
    "    def __init__(self, class_num, nums_block_list = [3, 4, 6, 3], strides_list = [1, 2, 2, 2]):\n",
    "        super(SKNet, self).__init__()\n",
    "        self.basic_conv = nn.Sequential(\n",
    "            nn.Conv2d(3, 64, 7, 2, 3, bias=False),\n",
    "            nn.BatchNorm2d(64),\n",
    "            nn.ReLU(inplace=True),\n",
    "        )\n",
    "        \n",
    "        self.maxpool = nn.MaxPool2d(3,2,1)\n",
    "        \n",
    "        self.stage_1 = self._make_layer(64, 64, 64, nums_block=nums_block_list[0], stride=strides_list[0])\n",
    "        self.stage_2 = self._make_layer(64, 128, 128, nums_block=nums_block_list[1], stride=strides_list[1])\n",
    "        self.stage_3 = self._make_layer(128, 256, 256, nums_block=nums_block_list[2], stride=strides_list[2])\n",
    "        self.stage_4 = self._make_layer(256, 512, 512, nums_block=nums_block_list[3], stride=strides_list[3])\n",
    "     \n",
    "        self.gap = nn.AdaptiveAvgPool2d((1, 1))\n",
    "        self.classifier = nn.Linear(512, class_num)\n",
    "        \n",
    "    def _make_layer(self, in_feats, mid_feats, out_feats, nums_block, stride=1):\n",
    "        layers=[SKUnit(in_feats, mid_feats, out_feats, stride=stride)]\n",
    "        for _ in range(1,nums_block):\n",
    "            layers.append(SKUnit(out_feats, mid_feats, out_feats))\n",
    "        return nn.Sequential(*layers)\n",
    "\n",
    "    def forward(self, x):\n",
    "        fea = self.basic_conv(x)\n",
    "        fea = self.maxpool(fea)\n",
    "        fea = self.stage_1(fea)\n",
    "        fea = self.stage_2(fea)\n",
    "        fea = self.stage_3(fea)\n",
    "        fea = self.stage_4(fea)\n",
    "        fea = self.gap(fea)\n",
    "        fea = torch.squeeze(fea)\n",
    "        fea = self.classifier(fea)\n",
    "        return fea\n",
    "\n",
    "def SKNet26(nums_class=100):\n",
    "    return SKNet(nums_class, [2, 2, 2, 2])\n",
    "def SKNet50(nums_class=100):\n",
    "    return SKNet(nums_class, [3, 4, 6, 3])\n",
    "def SKNet101(nums_class=100):\n",
    "    return SKNet(nums_class, [3, 4, 23, 3])\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "c360daec",
   "metadata": {
    "execution": {
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    },
    "executionInfo": {
     "elapsed": 680,
     "status": "ok",
     "timestamp": 1684414637992,
     "user": {
      "displayName": "RUIYING CHEN",
      "userId": "09384040230747805046"
     },
     "user_tz": -480
    },
    "id": "otA9CIgdnXfW",
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    "tags": []
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   "outputs": [],
   "source": [
    "device=\"cuda\"\n",
    "net=SKNet26().to(device)\n",
    "#net=ResidualNet(\"CIFAR100\",50,100,None).to(device)\n",
    "#print(net)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "800c5be8",
   "metadata": {
    "execution": {
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    "executionInfo": {
     "elapsed": 8,
     "status": "ok",
     "timestamp": 1684414638807,
     "user": {
      "displayName": "RUIYING CHEN",
      "userId": "09384040230747805046"
     },
     "user_tz": -480
    },
    "id": "35VI01P_kqal",
    "outputId": "64747940-f10a-4982-df2f-d5bc5a500b78",
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5000\n"
     ]
    }
   ],
   "source": [
    "import torch.optim as optim\n",
    "\n",
    "criterion = nn.CrossEntropyLoss()\n",
    "optimizer = torch.optim.Adam(net.parameters(), lr=0.0005)\n",
    "print(len(train_loader))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0e4185dd",
   "metadata": {
    "executionInfo": {
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     "status": "ok",
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   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "e3793877",
   "metadata": {
    "execution": {
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    "executionInfo": {
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      "displayName": "RUIYING CHEN",
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   "outputs": [],
   "source": [
    "from tqdm import tqdm\n",
    "def train(epoch):\n",
    "    net.train()\n",
    "    # Loop over each batch from the training set\n",
    "    train_tqdm = tqdm(train_loader, desc=\"Epoch \" + str(epoch))\n",
    "    for batch_idx, (data, target) in enumerate(train_tqdm):\n",
    "        # Copy data to GPU if needed\n",
    "        data = data.to(device)\n",
    "        target = target.to(device)\n",
    "        # Zero gradient buffers\n",
    "        optimizer.zero_grad()\n",
    "        # Pass data through the network\n",
    "        output = net(data)\n",
    "        # Calculate loss\n",
    "        loss = criterion(output, target)\n",
    "        # Backpropagate\n",
    "        loss.backward()\n",
    "        # Update weights\n",
    "        optimizer.step()  # w - alpha * dL / dw\n",
    "        train_tqdm.set_postfix({\"loss\": \"%.3g\" % loss.item()})\n",
    "\n",
    "def validate(lossv,top1AccuracyList,top5AccuracyList):\n",
    "    net.eval()\n",
    "    val_loss = 0\n",
    "    top1Correct = 0\n",
    "    top5Correct = 0\n",
    "    for index,(data, target) in enumerate(test_loader):\n",
    "        data = data.to(device)\n",
    "        labels = target.to(device)\n",
    "        outputs = net(data)\n",
    "        val_loss += criterion(outputs, labels).data.item()\n",
    "        _, top1Predicted = torch.max(outputs.data, 1)\n",
    "        top5Predicted = torch.topk(outputs.data, k=5, dim=1, largest=True)[1]\n",
    "        top1Correct += (top1Predicted == labels).cpu().sum().item()\n",
    "        label_resize = labels.view(-1, 1).expand_as(top5Predicted)\n",
    "        top5Correct += torch.eq(top5Predicted, label_resize).view(-1).cpu().sum().float().item()\n",
    "\n",
    "    val_loss /= len(test_loader)\n",
    "    lossv.append(val_loss)\n",
    "\n",
    "    top1Acc=100*top1Correct / len(test_loader.dataset)\n",
    "    top5Acc=100*top5Correct / len(test_loader.dataset)\n",
    "    top1AccuracyList.append(top1Acc)\n",
    "    top5AccuracyList.append(top5Acc)\n",
    "    \n",
    "    print('\\nValidation set: Average loss: {:.4f}, Top1Accuracy: {}/{} ({:.0f}%) Top5Accuracy: {}/{} ({:.0f}%)\\n'.format(\n",
    "        val_loss, top1Correct, len(test_loader.dataset), top1Acc,top5Correct, len(test_loader.dataset), top5Acc))\n",
    "#     accuracy = 100. * correct.to(torch.float32) / len(testloader.dataset)\n",
    "#     accv.append(accuracy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "676e966f",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-05-24T01:18:41.070183Z",
     "iopub.status.busy": "2023-05-24T01:18:41.069393Z",
     "iopub.status.idle": "2023-05-24T02:31:51.383695Z",
     "shell.execute_reply": "2023-05-24T02:31:51.382447Z"
    },
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    "outputId": "b7c77e1e-40cc-417b-f5de-7467800712dd",
    "papermill": {
     "duration": 4390.323511,
     "end_time": "2023-05-24T02:31:51.385838",
     "exception": false,
     "start_time": "2023-05-24T01:18:41.062327",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 0: 100%|██████████| 5000/5000 [03:28<00:00, 23.97it/s, loss=3.43]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Validation set: Average loss: 4.0354, Top1Accuracy: 1187/10000 (12%) Top5Accuracy: 3486.0/10000 (35%)\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 1: 100%|██████████| 5000/5000 [03:12<00:00, 25.95it/s, loss=3.21]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Validation set: Average loss: 3.6601, Top1Accuracy: 1556/10000 (16%) Top5Accuracy: 4060.0/10000 (41%)\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 2: 100%|██████████| 5000/5000 [03:13<00:00, 25.86it/s, loss=3.83]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Validation set: Average loss: 3.1910, Top1Accuracy: 2276/10000 (23%) Top5Accuracy: 5059.0/10000 (51%)\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 3: 100%|██████████| 5000/5000 [03:14<00:00, 25.65it/s, loss=3.13]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Validation set: Average loss: 3.0998, Top1Accuracy: 2463/10000 (25%) Top5Accuracy: 5529.0/10000 (55%)\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 4: 100%|██████████| 5000/5000 [03:16<00:00, 25.38it/s, loss=2.79]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Validation set: Average loss: 2.8623, Top1Accuracy: 2866/10000 (29%) Top5Accuracy: 5875.0/10000 (59%)\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 5: 100%|██████████| 5000/5000 [03:18<00:00, 25.20it/s, loss=3.34]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Validation set: Average loss: 2.8876, Top1Accuracy: 3013/10000 (30%) Top5Accuracy: 5975.0/10000 (60%)\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 6: 100%|██████████| 5000/5000 [03:20<00:00, 24.99it/s, loss=2.72]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Validation set: Average loss: 2.7975, Top1Accuracy: 3186/10000 (32%) Top5Accuracy: 6229.0/10000 (62%)\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 7: 100%|██████████| 5000/5000 [03:22<00:00, 24.69it/s, loss=3.46]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Validation set: Average loss: 2.8336, Top1Accuracy: 3000/10000 (30%) Top5Accuracy: 5997.0/10000 (60%)\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 8: 100%|██████████| 5000/5000 [03:24<00:00, 24.39it/s, loss=2.61]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Validation set: Average loss: 2.8647, Top1Accuracy: 3402/10000 (34%) Top5Accuracy: 6406.0/10000 (64%)\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 9: 100%|██████████| 5000/5000 [03:19<00:00, 25.12it/s, loss=2.27]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Validation set: Average loss: 2.6810, Top1Accuracy: 3494/10000 (35%) Top5Accuracy: 6526.0/10000 (65%)\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 10: 100%|██████████| 5000/5000 [03:18<00:00, 25.13it/s, loss=1.35]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Validation set: Average loss: 2.7644, Top1Accuracy: 3600/10000 (36%) Top5Accuracy: 6602.0/10000 (66%)\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 11: 100%|██████████| 5000/5000 [03:20<00:00, 25.00it/s, loss=2.43]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Validation set: Average loss: 2.6926, Top1Accuracy: 3637/10000 (36%) Top5Accuracy: 6587.0/10000 (66%)\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 12: 100%|██████████| 5000/5000 [03:20<00:00, 24.99it/s, loss=1.29]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Validation set: Average loss: 3.4105, Top1Accuracy: 3553/10000 (36%) Top5Accuracy: 6386.0/10000 (64%)\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 13: 100%|██████████| 5000/5000 [03:20<00:00, 24.98it/s, loss=3.15]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Validation set: Average loss: 2.6701, Top1Accuracy: 3578/10000 (36%) Top5Accuracy: 6597.0/10000 (66%)\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 14: 100%|██████████| 5000/5000 [03:21<00:00, 24.78it/s, loss=2.08]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Validation set: Average loss: 2.5245, Top1Accuracy: 3853/10000 (39%) Top5Accuracy: 6818.0/10000 (68%)\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 15: 100%|██████████| 5000/5000 [03:23<00:00, 24.61it/s, loss=1.96]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Validation set: Average loss: 2.7576, Top1Accuracy: 3821/10000 (38%) Top5Accuracy: 6687.0/10000 (67%)\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 16: 100%|██████████| 5000/5000 [03:23<00:00, 24.53it/s, loss=1.78]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Validation set: Average loss: 2.5756, Top1Accuracy: 3875/10000 (39%) Top5Accuracy: 6842.0/10000 (68%)\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 17: 100%|██████████| 5000/5000 [03:25<00:00, 24.36it/s, loss=1.02]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Validation set: Average loss: 2.5223, Top1Accuracy: 3971/10000 (40%) Top5Accuracy: 6842.0/10000 (68%)\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 18: 100%|██████████| 5000/5000 [03:20<00:00, 24.99it/s, loss=1.96]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Validation set: Average loss: 2.9597, Top1Accuracy: 3884/10000 (39%) Top5Accuracy: 6751.0/10000 (68%)\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 19: 100%|██████████| 5000/5000 [03:18<00:00, 25.20it/s, loss=1.43]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Validation set: Average loss: 3.6201, Top1Accuracy: 3781/10000 (38%) Top5Accuracy: 6625.0/10000 (66%)\n",
      "\n",
      "Finished Training\n"
     ]
    }
   ],
   "source": [
    "lossv = []\n",
    "top1AccuracyList = []   # top1准确率列表\n",
    "top5AccuracyList = []   # top5准确率列表\n",
    "max_epoch = 20\n",
    "for epoch in range(max_epoch):  # loop over the dataset multiple times\n",
    "    train(epoch)\n",
    "    with torch.no_grad():\n",
    "        validate(lossv,top1AccuracyList,top5AccuracyList)\n",
    "        \n",
    "print('Finished Training')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "1a7d5150",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-05-24T02:32:13.265568Z",
     "iopub.status.busy": "2023-05-24T02:32:13.265140Z",
     "iopub.status.idle": "2023-05-24T02:32:13.269720Z",
     "shell.execute_reply": "2023-05-24T02:32:13.268736Z"
    },
    "executionInfo": {
     "elapsed": 22810,
     "status": "ok",
     "timestamp": 1684424352765,
     "user": {
      "displayName": "RUIYING CHEN",
      "userId": "09384040230747805046"
     },
     "user_tz": -480
    },
    "id": "Hag0zV9cmHm_",
    "outputId": "1434ac5c-f76c-4637-9223-e6955c942441",
    "papermill": {
     "duration": 11.076367,
     "end_time": "2023-05-24T02:32:13.271821",
     "exception": false,
     "start_time": "2023-05-24T02:32:02.195454",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# from google.colab import drive\n",
    "# drive.mount('/content/drive')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "1aa13045",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-05-24T02:32:35.413429Z",
     "iopub.status.busy": "2023-05-24T02:32:35.413054Z",
     "iopub.status.idle": "2023-05-24T02:32:36.162861Z",
     "shell.execute_reply": "2023-05-24T02:32:36.161971Z"
    },
    "id": "Z1-D3LkhvzDq",
    "papermill": {
     "duration": 11.481164,
     "end_time": "2023-05-24T02:32:36.164947",
     "exception": false,
     "start_time": "2023-05-24T02:32:24.683783",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'validation top5 accuracy')"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 500x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 500x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 500x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "# 绘制曲线\n",
    "plt.figure(figsize=(5, 3))\n",
    "plt.plot(np.arange(1, max_epoch + 1), lossv)\n",
    "plt.title('validation loss')\n",
    "\n",
    "plt.figure(figsize=(5,3))\n",
    "plt.plot(np.arange(1, max_epoch + 1), top1AccuracyList)\n",
    "plt.title('validation top1 accuracy')\n",
    "\n",
    "plt.figure(figsize=(5,3))\n",
    "plt.plot(np.arange(1, max_epoch + 1), top5AccuracyList)\n",
    "plt.title('validation top5 accuracy')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7f6fc830",
   "metadata": {
    "id": "fyWGvlbHup5w",
    "papermill": {
     "duration": 11.339315,
     "end_time": "2023-05-24T02:32:58.243822",
     "exception": false,
     "start_time": "2023-05-24T02:32:46.904507",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.10"
  },
  "papermill": {
   "default_parameters": {},
   "duration": 4513.711775,
   "end_time": "2023-05-24T02:33:11.798866",
   "environment_variables": {},
   "exception": null,
   "input_path": "__notebook__.ipynb",
   "output_path": "__notebook__.ipynb",
   "parameters": {},
   "start_time": "2023-05-24T01:17:58.087091",
   "version": "2.4.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
